Convolutional neural network-based automatic detection of follicle cells in ovarian tissue using optical coherence tomography.

To preserve the fertility of young female cancer patients, ovarian tissue cryopreservation and transplantation have been investigated as next-generation reproductive medical technologies. Non-invasive visualization of follicles in ovarian tissue and cryopreservation of higher density tissue is essential for effective transplantation. We proposed the use of optical coherence tomography (OCT) that can noninvasively visualize the internal structure of the ovarian tissue. However, a method for quantifying cell density has not yet been established because of the lack of available techniques to visualize follicles noninvasively. We proposed the use of a convolutional neural network (CNN) to extract small features from medical images as an image analysis method to automatically detect follicles from the obtained OCT images. First, we collected a total of 13 ovarian tissues from four-day-old mice and acquired OCT images using a full-field-type OCT. Then, the acquired images were analyzed using three detection methods: filter processing, filter processing combined with the CNN, and only CNN. Finally, to verify the detection accuracy of each method, the detection rate and precision were calculated by taking the doctor's detection as the correct result. The results showed that the detection method only using CNN achieved a detection rate of 0.81 and precision of 0.67; this indicated that follicles could be effectively detected using our proposed method. Furthermore, it is quantitatively evident that the density of follicles from the surface layer to the deep region differs depending on the tissue. In the future, these results could be used to detect follicles in tissues of different maturation stages and quantify follicles three-dimensionally, further accelerating next-generation reproductive medicine.

Preliminary human application of optical coherence tomography for quantification and localization of primordial follicles aimed at effective ovarian tissue transplantation.

PURPOSE: The purpose of this study was to evaluate the possible clinical application of optical coherence tomography for assessing ovarian reserve in individual specimens of human ovarian tissue for fertility preservation. METHODS: Ovarian tissue examination by optical coherence tomography was performed before ovarian tissue cryopreservation. Three of the four subjects had hematological disease or cancer, and they faced a threat to their fertility due to impending chemotherapy. One patient underwent ovarian tissue extraction for in vitro activation of dormant follicles as fertility treatment. RESULTS: The current full-field optical coherence tomography technique can detect primordial follicles in non-fixed and non-embedded human ovarian tissue. These images are well correlated with histological evaluation and the ovarian reserve test, including follicle counts. CONCLUSION: It was demonstrated that optical coherence tomography could assess localization of primordial follicles and ovarian reserve in specimens of non-fixed human ovarian cortex, although optimization for examination of human ovarian tissue is needed for clinical application. Additionally, this technique holds the possibility of assessing the ovarian reserve of patients with unevaluable ovarian reserve. TRIAL REGISTRATION NUMBER: UMIN000023141.